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Clustering comes under unsupervised learning algorithm :  for clustering algorithm uses are  : k- mean clustering   clustering is the process of partitioning group of data points into small number of clusters k means is a clustering method that aims to find the position of the  cluster that minimize the distance from data points to the  cluster k-means clustering is one of the simplest algorithms which uses unsupervised learning method to solve known clustering issues. k-means clustering require following two inputs. k = number of clusters Training set(m) = {x1, x2, x3,……….., xm} Let’s say you have an unlabeled data set like the one shown below and you want to group this data into clusters. take mean value find nearest number and mean and put into cluster repeat one and two until we not get same mean example : {2,3,4,10,11,12,20,25,30} data set creating two cluster m1=4 m2=12 k1...

Here is Steps  : First Download python from Python.org cd downloads ls ./configure sudo make sudo make install sudo apt-get install  libssl.dev openssl Enjoy : 

Learn Python for data science  ; Java  -  int a=10;             int b=20            int temp; temp=a; a=b; b=temp; ===================================================== x=10 y=20 x,y=y,x ======================================================= String name="GOVIND" SOP(name) ================= English Statement : name="Govind" print(name) ====================================  

Let A is the square matrix of order n*n ,then a number  lambda is set to be Eigen value of matrix A if there exists a column matrix  X of order   n*1 such that AX=lambdaX It menas that , if column matrix A , A*X= lambda*X then  lmbda is called eigen value of  A. X-eigen vector of A.  AX-lambdaX=0 A-(Lambda*I )X=0 Eigen Value - chrecteristics value Eigen Vector- Characteristic Vecor Working rule to find Eigen Value and Eigen Vector : First u need to create characteristic equations of matrix A, it is determinate of  | A-lambda *I| = 0 I - is the square matrix . Solutions of characteristics equations  is called Eigen values , on the basis of egen values  corespoing we can find egen vector .

- It is the Study of two variables. - One variable is called as In Dependent variable.(x) - And Other Variable is called as Dependent Variable(y)  Example : Salary dependent on Number Of experience U have. - So in this example we can say that : Salary  is dependent variable    and Experience is Independent variable. - It is used for predictions.  - Here  we try to predict  the change in dependent variable according to change in independent variable. Regression    y=a+bx; Here y is dependent variable and x is called as Independent Variable .                                  In this example sales is dependent on price  : sales dependents on prince  In this Example Salary is Dependent Variable and Number of Years of Experience is Independe...

    Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data.   One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.     Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents.    And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it.    Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how m...